onnxcustom: custom ONNX
Examples, tutorial on how to convert machine learned models into ONNX, implement your own converter or runtime, or even train with ONNX / onnxruntime.
The function check or the command line python -m onnxcustom check checks the module is properly installed and returns processing time for a couple of functions or simply:
import onnxcustom onnxcustom.check()
The documentation also introduces onnx, onnxruntime for inference and training. The tutorial related to scikit-learn has been merged into sklearn-onnx documentation. Among the tools this package implements, you may find:
a tool to convert NVidia Profilder logs into a dataframe,
a SGD optimizer similar to what scikit-learn implements but based on onnxruntime-training and able to train an CPU and GPU,
functions to manipulate onnx graph.
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Release files for onnxcustom 0.4.293
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Release files / onnxcustom-0.4.293.tar.gz
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